Searching for macro operators with automatically generated heuristics

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2056, Issue , 2001, Pages 194-203

  Hernádvölgyi, István T ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

AUTOMATICALLY GENERATED; MACRO-OPERATORS; OPTIMALITY; SEARCH SPACES; SPEED UP; SUBGOALS;

OPTIMIZATION;

EID: 23044526295     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45153-6_19     Document Type: Conference Paper

References (12)

1. G. Butler. Fundamental Algorithms for Permutation Groups. Lecture Notes in Computer Science. Springer-Verlag, 1991.
2. J. C. Culberson and J. Schaeffer. Searching with pattern databases. Advances in Artificial Intelligence (Lecture Notes in Artificial Intelligence 1081), pages 402-416, 1996.
3. I. T. Hernádvölgyi and R. C. Holte. PSVN: A vector representation for production systems. Technical Report TR-99-04, School of Information Technology and Engineering, University of Ottawa, 1999.
4. I. T. Hernádvölgyi and R. C. Holte. Experiments with automatically created memory-based heuristics. In Abstraction, Reformulation and Approximation, volume 1864 of Lecture Notes in Artificial Intelligence, pages 281-290, 2000.
5. R. C. Holte and I. T. Hernádvölgyi. A space-time tradeoff for memory-based heuristics. Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pages 704-709, 1999.
6. R. E. Korf. Learning to Solve Problems by Searching for Macro-Operators. PhD thesis, Canegie-Mellon University, 1983.
7. R. E. Korf. Operator decomposability: A new type of problem structure. Proceedings of the National Conference on Artificial Intelligence (AAAI-83), pages 206-209, 1983.
8. R. E. Korf. Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence, 27:97-109, 1985.
9. R. E. Korf. Macro-operators: A weak method for learning. Artificial Intelligence, 26:35-77, 1985.
10. R. E. Korf. Finding optimal solutions to Rubik’s Cube using pattern databases. Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), pages 700-705, 1997.
11. A. E. Prieditis. Machine discovery of effective admissible heuristics. Machine Learning, 12:117-141, 1993.
12. C. C. Sims. Computational methods in the study of permutation groups. Computational Problems in Abstract Algebra, pages 169-183, 1970.